2025-11-28 21:03:16
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Abbott Laboratories is a $220 billion medical device giant. But the company is far more than devices. Abbott also generates billions from diagnostics, nutrition, and established pharmaceuticals, making it one of the most diversified healthcare companies in the world.
Now Abbott is acquiring Exact Sciences, the maker of the blockbuster Cologuard colorectal cancer screening test, in a $21 billion all-cash deal expected to close in Q2 2026. The acquisition says a lot about where the diagnostics segment is headed.
Exact Sciences isn’t a typical bolt-on. Cologuard has become one of the most successful at-home diagnostic products ever, generating over $2 billion annually, growing double digits, and backed by one of healthcare’s most reliable recurring revenue streams: screening compliance.
For Abbott, this adds a sticky, high-margin diagnostics business with huge cross-sell potential. As CEO Robert Ford put it: “Our vision here is really to build the premier cancer diagnostics company in the world.”
Cologuard thrives because of provider relationships, patient awareness, and ease of use. Abbott excels at all three.
Abbott can push Cologuard through its global distribution, bundle it with existing diagnostics, and potentially replicate the model across other cancer-screening categories Exact has been developing. It’s the same playbook that turned FreeStyle Libre into a category-defining product.
Cancer screening is recurring. Diagnostics are predictable. Cologuard has brand recognition that most tests never achieve. And Abbott has the balance sheet and sales force to scale it globally.
Exact Sciences is projected to generate more than $3 billion in revenue this year, which will push Abbott’s diagnostics revenue above $12 billion annually once the deal closes.
Here’s what Exact’s margin profile looks like. The dramatic dip in Q4 FY24 came from large one-time charges, impairment write-downs, and restructuring costs. Gross margins stayed steady, and operating margin quickly rebounded, confirming the core business was intact.
This deal fits a broader trend of healthcare incumbents buying proven products with reliable, repeatable demand rather than funding moonshot R&D from scratch.
Recent examples include:
Johnson & Johnson acquiring Shockwave Medical (~$13 billion) for high-growth cardiovascular devices.
Danaher acquiring Abcam (~$6 billion) to strengthen its antibody and reagent portfolio.
Thermo Fisher buying Olink (~$3 billion) to expand proteomics and biomarker capabilities.
In uncertain markets, incumbents buy certainty instead of chasing moonshots. Abbott itself has the balance sheet capacity for up to $30 billion in debt-funded M&A, thanks to low leverage and $7 billion in annual free cash flow.
AI skepticism has resurfaced in a big way. So much so that Michael Burry of “Big Short” fame launched a paid Substack arguing that NVIDIA is the new Cisco, or that US Big Tech is engaged in accounting fraud. Both are highly questionable claims, but they capture the moment.
As massive AI infrastructure commitments circulate through the market, including OpenAI’s multi‑year buildout, investors must answer a simple question: Who is actually on the hook for delivering all this compute? And how much of that future demand is already baked into today’s stock prices?
Public companies exposed to the AI supercycle are now being repriced based on execution risk. After all, if a stock surged on inflated expectations, the risk of a round trip is real.
A popular chart making the rounds on X this week was the stock price performance of the Google universe (including GOOG and AVGO due to Broadcom’s role in manufacturing Google’s TPU chips) compared to the OpenAI universe (NVDA, AMD, ORCL, MSFT, and CRVW benefiting from demand from the AI startup). The market keeps repricing based on the latest information available.
Oracle has been at the center of the biggest AI story of the year: OpenAI’s $1.4 trillion multi-year commitments. Few companies have tied their future to that wave as aggressively as Oracle.
Oracle’s latest quarter showed a stunning $455 billion in remaining performance obligations (RPO). Management tied hundreds of billions of that to long-term AI cloud deals, with OpenAI front and center.
But here’s the nuance:
RPO is not cash.
RPO is not revenue.
RPO is not even demand.
It’s a mix of multi-year capacity agreements, partial non-cancellable commitments, and cloud reservations that reflect intent rather than realized consumption.
Oracle’s stock price doubled from January to September, driven by the sheer scale of these AI commitments. Now the stock has fallen back to earth (down more than 40% in the past two months) as the market questions how much of this will convert, and how quickly.
The massive backlog announcement came from Project Stargate, which was already well documented. You could argue the stock rally was never justified to begin with, since the size of the contract was already well known. But what matters now is that the market shaved off the entire spike, and then some.
In short, skepticism is reflected in the stock price.
Oracle is not like the rest of Big Tech. It’s investing ahead of hypothetical revenue. Unlike hyperscalers with massive existing free cash flow funding their AI buildouts, Oracle is leveraging up to finance a rapid global expansion of data centers.
Key pressures include:
Higher debt levels.
Capex rising faster than cash flow.
Tight financial flexibility compared to peers.
This works brilliantly if everything clicks, but it creates a real downside if it doesn’t.
Microsoft, Amazon, Google, and Meta are funding AI infrastructure from strong balance sheets and operating cash flow. Demand is diversified across existing product lines like ad performance, enterprise cloud spend, government workloads, and SMB adoption.
Below is a 30,000-foot view of their annual free cash flow in the past decade, with the 2025 data as of September (trailing 12 months). And remember, free cash flow is already net of CapEx, so the impact of the so-called runaway spending is already baked into these numbers.
Their valuation is anchored on existing cash flow, not a hypothetical future. Amazon is an outlier in the chart above because the company is in perpetual ‘Day 1’ mode, reinvesting operating cash flow in new initiatives like Project Kuiper (low-earth satellites).
Could AWS, Azure, and Google Cloud see softer demand at some point if large AI startups fail to find product-market fit? Possibly. But if you are a regular reader of our earnings breakdowns, you know that many Enterprise customers are already seeing meaningful revenue growth from AI features. On the consumer side, Google and Meta have improved their products with AI (engagement, ad conversion), boosting revenue growth and margins.
Oracle, by contrast, has:
The biggest explicit AI-startup-related backlog.
The least balance sheet and cash flow flexibility.
The highest sensitivity to any slowdown in AI demand.
That’s why the market is going to treat AI exposure with different levels of scrutiny.
The AI supercycle is real. But when it comes to what’s priced in, investors are now focused on execution. Oracle’s latest round trip shows that the market has already adjusted some valuations, giving a premium to tangible earnings growth and looking at hypothetical long-term upside with caution.
And you know what? For a market that’s been deemed frothy for a while, a healthy sell-off in inflated stocks is just what the doctor ordered.
That’s it for today.
Happy investing!
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Disclosure: I own AAPL, AMD, GOOG, AMZN, META, and NVDA in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members.
2025-11-26 06:43:52
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🪙 Crypto: Circle, Bullish.
📢 CRM: HubSpot, Veeva.
🚗 Automotive: Ferrari NIO.
👟 Apparel: Amer Sports, On.
☁️ Hyperscalers: CoreWeave.
🔒 Security: Dynatrace, Fortinet.
📊 Data: Datadog, Elastic, Palantir.
👔 Buffett: Berkshire, Chevron, Oxy.
⚙️ Chip design: AMD, ARM, NVIDIA.
🏈 Sports betting: DraftKings, Flutter.
🏍️ Gig economy: Lyft, Instacart, Uber.
💬 Social: Match Group, Pinterest, Reddit.
📢 Advertising: Applovin, The Trade Desk.
📦 Commerce software: Global-e, Shopify.
🔬 Semis: Applied Materials, Analog Devices.
📱 Subscription: Duolingo, Peloton, The NYT.
🌐 Networks: Arista, Cisco, Nutanix, Palo Alto.
✈️ Travel: Airbnb, Expedia, Marriott, Tripadvisor.
💊 Pharma: Amgen, Eli Lilly, Pfizer, Novo Nordisk.
💻 Hardware: Dell, HP, Lenovo, Samsung, Xiaomi.
🛒 Retail: Walmart, Home Depot, Target, Best Buy.
🌯 Franchises: McDonald’s, Yum!, Restaurant Brands.
🎮 Gaming: NetEase, Nintendo, Sony, Take-Two, Tencent.
🍿 Entertainment: Disney, Live Nation, Paramount, Warner.
🛍️ E-commerce: Alibaba, Coupang, MercadoLibre, PDD, Sea.
💳 Payments: Affirm, Block, Chime, Dlocal, Global Payments, Nu, Toast.
☁️ Enterprise Software: Atlassian, Autodesk, Axon, C3.ai, Digital Ocean, Docebo, Figma, Intuit, Klaviyo, Monday, Paycom, Procore, Twilio, Workday, Zoom.
And more, like Hims & Hers, Tempus, Warby, Celsius, Zillow, …
2025-11-22 23:00:18
Welcome to the Saturday PRO edition of How They Make Money.
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Today at a glance:
🛒 Walmart: E-Commerce Surges
📦 PDD: Headwinds Ahead
✅ Intuit: OpenAI Integration
🔒 Palo Alto: AI Brings Customer Wins
🎮 NetEase: Mixed Beyond Gaming
🧑⚕️ Veeva: AI Platform Deepens Moat
🎯 Target: Digital Push
💻 Lenovo: AI Demand Sets Records
⛷️ Amer Sports: Arc’teryx Re-accelerates
💳 Klarna: Fair Financing Drives GMV
🔍 Elastic: AI Drives Adoption
🛍️ Global-e: Profitable Scale
🪙 Bullish: Institutional Pivot
Walmart sales grew 6% Y/Y to $179.5 billion ($4.3 billion beat) and GAAP EPS of $0.62 ($0.02 beat) for its October quarter (Q3 FY26).
Walmart US comps rose 4.5%, and international sales were up 11% in constant currency. Once again, e-commerce took the cake, up 27% globally, with all online segments growing over 20%. Higher-income shoppers, strong grocery momentum, and improving general merchandise all contributed. E-commerce sales now represent nearly 22% of Walmart’s revenue.
Advertising surged 53% Y/Y globally (including Vizio), and membership revenue grew 9%, boosting gross margin.
Adjusted operating income rose 8% in constant currency, and year-to-date operating cash flow hit $27 billion. Walmart reiterated that disciplined inventory, lower shipping costs, and smarter assortment management helped offset tariff-related cost pressure.
Management raised full-year guidance, now expecting 4.8%–5.1% sales growth (up from 3.75%–4.75% previously), with FY26 EPS of $2.58–$2.63 (up from $2.52 to $2.62). The quarter also marked a major transition with US chief John Furner stepping in as CEO in February 2026, as CEO Doug McMillon entered his final quarter after transforming Walmart into a digital and AI-driven retailer.
While low-income consumers show mild moderation and category mix remains a margin headwind, Walmart continues to gain market share across income cohorts, integrate AI across operations, and expand its marketplace and delivery capabilities. The market approved with shares ticking up 5% post-earnings.
Temu’s parent, PDD Holdings, saw Q3 revenue rise 9% Y/Y to $15.2 billion ($90 million miss), continuing its slow pace amid a tough consumer environment in China. Looking at the growth by segment, Online Marketing Services (performance-based) grew 8% and Transaction Services (including merchant fees) rose 10%.
The group is aggressively investing in its ecosystem, deliberately sacrificing short-term profit. Higher fulfillment and processing costs continued to impact the gross margin. The operating margin compressed to 23% (from 24% a year ago). However, net profit still grew 17% to $4.1 billion, helped by lower marketing spend (28% of revenue, down from 31% a year ago).
PDD’s long-term investments continued with the “100 billion support program,” and R&D expenses surged 41%. Overseas, the Temu arm is stabilizing its US presence (monthly active users improved after a sharp Q2 plunge) but is now focused on expanding into Europe to offset the damage from the end of the US de minimis tax exemption.
Management reiterated that quarterly profitability will fluctuate and explicitly warned investors not to use Q3 results as guidance, emphasizing a strategic focus on building “intrinsic value” over short-term earnings. The balance sheet remains stellar, with cash and short-term investments reaching RMB 424 billion (~$60 billion), and barely any debt.
2025-11-21 21:03:24
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Concerns about runaway spending, circular financing, and shaky startup economics dominate the headlines. But NVIDIA’s quarter tells the other side of the story.
CEO Jensen Huang set the tone:
“There has been a lot of talk about an AI bubble. From our vantage point, we see something very different. As a reminder, NVIDIA is unlike any other accelerator. We excel at every phase of AI, from pre-training and post-training to inference. [...] The world is undergoing three massive platform shifts at once, the first time since the dawn of Moore’s Law.”
Those three shifts matter:
⚡️ Accelerated computing replaces CPUs.
🧠 Generative AI reshapes hyperscale workloads.
🤖 Agentic and physical AI create entirely new categories.
Because all three run on the same architecture, NVIDIA captures the compounding effect of the entire AI stack expanding at once.
With overwhelming demand through 2026, the near-term “AI bubble implosion” narrative looks increasingly disconnected from reality.
Let’s break down the quarter.
Today at a glance:
NVIDIA’s Q3 FY26.
Business highlights.
Key quotes from the call.
What to watch moving forward.
NVIDIA’s fiscal year ends in January, so the October quarter was Q3 FY26.
Huang said Blackwell sales were ‘off the charts.’ The chart below makes the point.
Revenue jumped +22% Q/Q and 62% Y/Y to $57.0 billion ($1.9 billion beat).
⚙️ Data Center +25% Q/Q and +66% Y/Y to $51.2 billion.
🎮 Gaming -1% Q/Q and +30% Y/Y to $4.3 billion.
👁️ Professional Viz +26% Q/Q and +56% Y/Y to $0.8 billion.
🚘 Automotive +1% Q/Q and +32% Y/Y to $0.6 billion.
🏭 OEM & Other +1% Q/Q and +79% Y/Y to $0.2 billion.
Gross margin was 73% (-1pp Y/Y).
Operating margin was 63% (+1pp Y/Y).
Non-GAAP operating margin was 66% (flat Y/Y).
Non-GAAP EPS $1.30 ($0.04 beat).
Operating cash flow was $23.8 billion (42% margin).
Free cash flow was $22.1 billion (39% margin).
Cash and cash equivalents: $60.6 billion.
Debt: $8.5 billion.
Revenue +14% Q/Q and +65% Y/Y to $65.0 billion ($3.2 billion beat).
Gross margin 75% (+1.4pp Q/Q).
⚙️ Data Center hit 90% of total revenue, growing 25% Q/Q to $51.2 billion, a massive acceleration. The Blackwell ramp broadened again, with hyperscalers, sovereign customers, and large enterprises all increasing deployments. Management noted strong adoption across training, inference, and early agentic workloads. Supply remains the gating factor, not demand:
⚡ Compute revenue surged 27% Q/Q as Blackwell availability improved and large projects moved into production. The absence of China revenue is now fully baked into the baseline. The growth you’re seeing is entirely ex-China, an important signal that the AI cycle no longer depends on China at the margin.
🔌 Networking rose 13% Q/Q, reflecting the build-out of AI factories. CFO Colette Kress highlighted continued strength in Spectrum-X Ethernet, InfiniBand, and NVLink as clusters get denser and model complexity rises. Networking is becoming a structural growth engine.
🎮 Gaming was mostly flat Q/Q at $4.3 billion, lapping the Blackwell-powered GPU launch last quarter but still up 30% Y/Y. GeForce maintains strong demand, even as the company reallocates more supply toward Data Center.
👁️ Professional visualization grew 26% Q/Q, benefiting from workstation upgrades and AI-accelerated design workflows. This segment continues to recover as enterprises modernize their tooling.
🚘 Automotive was steady, up 1% Q/Q, reflecting gradual adoption of NVIDIA’s autonomous driving and digital cockpit platforms.
📉 Margins expanded again, with gross margin climbing sequentially to 73% and Q4 guided even higher to 75%. The mix continues to improve as networking scales and Blackwell availability normalizes.
🔮 The outlook is exceptional: Q4 revenue is guided up another 14% sequentially to $65 billion ($3.2 billion ahead of estimates) despite assuming zero shipments to China.
Big picture: NVIDIA is growing at breakneck speed, and the ramp is now powered by two engines: Blackwell compute and the networking fabric behind AI factories. The China reset is already absorbed, and the cycle is being driven by global AI infrastructure demand that continues to broaden, deepen, and compound.
The past quarter was a blur of mega-partnerships, each one expanding NVIDIA’s reach deeper into the AI stack.
OpenAI: Working with NVIDIA to build and deploy at least 10 GW of AI data centers. NVIDIA plans to take an equity stake and invest up to $100 billion over time as part of the multi-year buildout.
Anthropic: Signed a deep platform deal to run up to 1 gigawatt of Grace Blackwell and Rubin systems, alongside a planned $10 billion investment from NVIDIA. Anthropic will purchase $30 billion of compute from Azure and collaborate with NVIDIA on model training and hardware optimization, turning a prior non-customer into a full-stack NVIDIA partner.
xAI: xAI is building gigawatt-scale Colossus 2 AI factories anchored on Blackwell, including a 500 MW flagship site with Humane. AWS will supply up to 150,000 NVIDIA accelerators to power these workloads.
Saudi Arabia (KSA): Framework agreement for roughly 400,000 to 600,000 GPUs over three years.
Palantir: Bringing CUDA X into Ontology, with customers like Lowe’s already using it for supply chain and analytics workflows.
Fujitsu, Intel, and Arm: Announced NVLink integrations that wire their CPU roadmaps directly into NVIDIA’s ecosystem.
The combined commitments of Microsoft, OpenAI, and Anthropic effectively ensure multi-year visibility for NVIDIA’s systems and keep demand anchored in the hyperscaler ecosystem.
Jensen Huang touched on the circular financing of customers:
“No company has grown at the scale that we’re talking about and have the connection and the depth and the breadth of supply chain that NVIDIA has. The reason why our entire customer base can rely on us is because we’ve secured a really resilient supply chain, and we have the balance sheet to support them.”
NVIDIA now has a Berkshire-style challenge to put its money to work. With a fast-growing pile of $61 billion in cash, the company is investing in the most important players in AI to secure future offtake and expand the CUDA ecosystem. These deals ensure that the fastest-growing AI companies have the resources to scale, which reinforces NVIDIA’s long-term demand rather than propping it up.
Custom silicon is rising across cloud providers: TPUs at Google, Trainium at AWS, Maia at Microsoft, and Meta’s internal accelerators. These chips target specific workloads, not the frontier or the broad platform layer.
Blackwell remains the default for large-scale training, inference, and agentic systems. Even alternative accelerators often rely on NVIDIA’s networking stack, reinforcing the systems moat rather than weakening it.
Customer ASICs shift negotiating leverage at the edges, but they expand the total compute pie, and NVIDIA remains essential at the center.
China has moved from a swing factor to a structural constraint. Q3 confirmed what the last two quarters hinted: H20 demand never materialized, B-series chips face fresh US scrutiny, and China’s regulators are directing state-backed data centers toward domestic accelerators. NVIDIA shipped just $50 million of H20 this quarter, effectively zero.
Management now assumes no China revenue in both Q4 and FY27 guidance. The real risk is long-term. China is racing to build a full domestic AI stack that bypasses CUDA entirely. If successful, a market Jensen once pegged at ~$50 billion becomes structurally closed. For now, the rest of the world is more than compensating.
Check out the earnings call transcript on Fiscal.ai here.
“We currently have visibility to $500 billion in Blackwell and Rubin revenue from the start of this year through the end of calendar year 2026. [...] We believe NVIDIA will be the superior choice for the $3 trillion-$4 trillion in annual AI infrastructure build we estimate by the end of the decade. Demand for AI infrastructure continues to exceed our expectations.”
This anchors the growth looking forward and frames the $500 billion Blackwell–Rubin pipeline inside a multi-trillion-dollar decade-long build-out.
“In the end, you still only have one gigawatt of power, one gigawatt data centers, one gigawatt of power. Therefore, performance per watt, the efficiency of your architecture, is incredibly important. [...] Your performance per watt translates directly to your revenues, which is the reason why choosing the right architecture matters so much now.”
Power is the binding constraint, and that perf-per-watt is the real battleground for economics that could favor NVIDIA over the long haul.
“NVIDIA’s architecture, NVIDIA’s platform is the singular platform in the world that runs every AI model.[...] We run OpenAI. We run Anthropic. We run xAI [...] We run Gemini [...] We run science models, biology models, DNA models, gene models, chemical models [...] AI is impacting every single industry.”
Huang’s ecosystem argument is simple: one architecture, every major model, across consumer apps, enterprises, and science. It reinforces CUDA as the default AI operating layer.
A growing concern on Wall Street is the useful life of high-end AI hardware.
Every new GPU generation makes the previous one look older faster, raising fears that hyperscalers will be forced to accelerate depreciation, pressuring long-term margins and EPS projections.
Colette Kress pushed back. She emphasized that software updates extend the useful life of NVIDIA GPUs and that the shift from simple chatbots to agentic AI dramatically increases compute intensity, keeping older clusters fully utilized even as Blackwell and Rubin ramp. The worry is real, but NVIDIA’s argument is that the demand curve determines effective lifespan.
Many super investors dialed back their NVIDIA enthusiasm in the latest 13F filings covering Q3 2025. It’s not entirely surprising with the stock hitting new highs. NVIDIA remains one of the most widely held names, yet many funds are still underweight relative to its nearly 8% weight in the S&P 500.
At ~32x forward earnings, NVIDIA trades mostly in line with the rest of Big Tech. But with EPS surging 67% Y/Y, you could argue NVIDIA looks cheap. NVIDIA’s growth is supply-constrained. That means quarter-to-quarter noise matters less than understanding how long this cycle can run and what the business looks like when demand normalizes.
If you’re a regular reader, you already know what to watch:
Cycles boom and bust: Like every major tech cycle, demand will eventually plateau or reset, and it will inevitably create volatility. However, in the words of Arya Stark: “Not today.”
Networking vs. compute mix: Is networking going to make a larger share of Data Center as AI factories scale?
Sovereign AI momentum: Are more countries committing to large, multi-year infrastructure builds?
China visibility: Does NVIDIA maintain a zero-China baseline, or is there any sign of reopening?
Hyperscaler silicon adoption: How quickly are cloud providers shifting toward in-house chips, and does it eventually impact NVIDIA's margins?
Power constraints: Energy efficiency and performance-per-watt remain the next competitive frontier, with many moving pieces.
Rubin cadence: Rubin is already in the lab. Any clarity on sampling, production, or deployment timing for the next platform will move the stock.
NVIDIA sits at the center of the most important computing cycle in decades. The details will shift (China, custom silicon, financing), but the direction hasn’t changed. As long as AI factories keep scaling, NVIDIA remains the one building the rails.
That’s it for today!
Happy investing!
Thanks to Fiscal.ai for being our official data partner. Create your own charts and pull key metrics from 50,000+ companies directly on Fiscal.ai. Save 15% with this link.
Disclosure: I own AAPL, AMD, AMZN, GOOG, META, and NVDA in App Economy Portfolio. I share my ratings (BUY, SELL, or HOLD) with App Economy Portfolio members.
Author's Note (Bertrand here 👋🏼): The views and opinions expressed in this newsletter are solely my own and should not be considered financial advice or any other organization's views.
2025-11-18 21:01:52
Welcome to the Premium edition of How They Make Money.
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Every quarter, funds managing over $100 million must disclose their portfolios, offering a rare glimpse into the minds of elite investors.
13F filings capture trades from July 1 to September 30. Q3 was a story of AI strength, rate uncertainty, and renewed market concentration around a handful of mega-cap winners (but not the ones you would expect). Against that backdrop, super investors sharpened their portfolios in surprisingly different ways.
Let’s see where the smart money leaned.
Today at a glance:
Hedge funds’ strategies.
Top buys and top holdings in Q3.
Google’s narrative reset.
Implications for individual investors.
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Before we dive into 13Fs, a quick reminder: blindly copying hedge fund trades is a terrible strategy.
Investing is like shooting 3-pointers. Even Steph Curry, the greatest shooter ever, misses more than half the time. There are no sure bets, even for the pros.
Your behavior matters more than your portfolio. As Peter Lynch said, “Know what you own and why you own it.”
Conviction is what helps you hold through volatility. And conviction comes from doing your own work, not borrowing someone else’s.
As Ian Cassel puts it:
“You can borrow someone else’s stock ideas but you can’t borrow their conviction. […] Do the work so you know when to sell. Do the work so you can hold. Do the work so you can stand alone.”
Some limitations of 13F filings:
Omit short positions and cash reserves.
Offer a partial view, leaving out smaller funds.
Exclude non-US equities, bonds, and commodities.
Can be dated, given their submission 45 days post-quarter.
With all this said, let’s see what top funds were buying and holding in Q2.
Hedge funds are financial powerhouses known for flexible, aggressive strategies designed to beat the market.
Here’s what typically shapes their moves:
Market conditions: Long in bull markets, defensive or short in bear markets.
Sector trends: Shifts in regulation or consumer behavior can steer capital.
Company fundamentals: Strong earnings, free cash flow, and leadership matter.
Macro factors: Rates, inflation, geopolitics—all influence positioning.
Quant models: Many funds lean on proprietary algorithms to find edge.
Risk management: Diversification, hedging, and position sizing are key.
Investor sentiment: Fear and greed can create mispriced opportunities.
Still, it doesn’t always work out.
The Global X Guru ETF (GURU), built to track top hedge fund holdings, has underperformed the S&P 500 over the past decade, even before fees.
And those fees matter. The classic “2 and 20” model (2% of assets + 20% of gains) can significantly reduce returns. It's no wonder that many individual investors are opting for simpler, lower-cost strategies.
Our partners at Fiscal.ai gather the data on Super Investors and visualize their portfolio for you. Pick your favorite investors and see how their holdings have evolved.
In early 2020, just before the COVID market turmoil, I curated a list of 20 top-performing hedge funds using TipRanks data. The selection focused on alpha relative to the S&P 500, and I also included a few funds frequently featured in my social feeds and podcast rotation. It’s not perfect, but it remains a solid directional filter.
The 10 stocks below represent half of the top holdings listed:
🤖 AI infrastructure: META, NVDA, TSM, GEV.
☁️ Hyperscalers: AMZN, MSFT, GOOG.
📦 Global commerce: MELI, SE, V.
This list doesn’t change much from one quarter to the next, so let’s turn to the more actionable insights with the new movements in Q3.
2025-11-15 23:01:25
Welcome to the Saturday PRO edition of How They Make Money.
Over 250,000 subscribers turn to us for business and investment insights.
In case you missed it:
📊 Monthly reports: 200+ companies visualized.
📩 Tuesday articles: Exclusive deep dives and insights.
📚 Access to our library: Hundreds of business breakdowns.
📩 Saturday PRO reports: Timely insights on the latest earnings.
Today at a glance:
📱 Tencent: Global Gaming Surge
🌐 Cisco: AI Orders Surge
🖥️ Sony: Anime Offsets Gaming Profit Dip
⚙️ Applied Materials: Preps for AI Ramp
🌊 Sea: Profit Miss Tarnishes Growth
🏦 Nu: AI-Fueled Credit
☁️ CoreWeave: Supply Stumbles
🚚 JD.com: New Biz Drags Margin
🏈 Flutter: FanDuel’s Prediction Pivot
🪙 Circle: Rate Sensitivity
👟 On: Accelerating Beyond Shoes
🥕 Instacart: Order Growth Accelerates
📆 Monday.com: Guidance Haunts Again
🌎 dLocal: Margins Squeezed
Tencent’s Q3 revenue rose 15% Y/Y to RMB 193 billion (~$27.2 billion), once again powered by a 23% rise in gaming. New blockbusters like Valorant Mobile (China’s biggest mobile launch of the year) and evergreen franchises like Honor of Kings and Clash Royale kept engagement high. Internationally, Tencent’s big bets are hitting: Delta Force surpassed 30 million DAUs, Dying Light: The Beast boosted PC revenues, and Supercell titles helped drive the huge overseas gaming beat, rising 43% Y/Y.
Net profit jumped 19% to RMB 65 billion, beating expectations. Capex fell 24% Y/Y to RMB 13B after a heavy AI build-out earlier this year. Free cash flow was flat at RMB 59 billion, while Tencent’s RMB 493 billion cash pile (including RMB 102 billion net cash) keeps it one of China’s strongest balance sheets.
Management reiterated its “measured AI” strategy, integrating its Hunyuan model into Weixin/WeChat, gaming, and ad systems rather than chasing splashy infrastructure spends. AI is already lifting ad ROAS, game engagement, and coding productivity. Weixin’s 1.41 billion MAUs continue to support monetization via mini-games, video accounts, and expanding payments (now including TenPay Global Checkout).
AI-boosted ad targeting pushed marketing services revenue up 21%. Cloud revenue improved as AI-related workloads ramped, though management cautioned that AI chip shortages limit how much compute they can rent out externally.
With AI upgrades rolling out across advertising and gaming, and a slate of major titles ahead, Tencent is delivering steady growth while sticking to margin discipline. All this, without spending aggressively on AI infrastructure like US Big Tech.
Cisco’s Q1 FY26 (September quarter) revenue rose 8% Y/Y to $14.9 billion ($100 million beat) and adjusted EPS was $1.00 ($0.02 beat).
AI infrastructure orders from hyperscalers surged to $1.3 billion in the September quarter (a massive uptick from $800 million in the June quarter). Products revenue rose 10% to $11.1 billion, driven by 15% growth in Networking.
Momentum in Networking (with product orders up 13%) and new AI partnerships (G42/UAE, NVIDIA N9100 switch) offset a temporary 2% decline in Security. Management attributed the security dip to platform transitions and a Splunk cloud-mix “timing issue.”
Cisco is now targeting $3 billion in AI revenue from hyperscalers for FY26, with a separate $2+ billion enterprise AI pipeline.
Cisco raised its full-year FY26 revenue guidance to $60.2–$61.0 billion (from $59–$60 billion) and adjusted EPS to $4.08–$4.14 (from $4.00–$4.06), citing accelerating AI demand. The Q2 outlook for both revenue and EPS also came in well ahead of consensus, as the company proves it can capture a significant share of the AI infrastructure spending.
